1 citations · 1 across the 3 of their papers we have counts for
3 papers
TT-MPD: Test Time Model Pruning and Distillation
Haihang Wu, Wei Wang, Tamasha Malepathirana +3
Pruning can be an effective method of compressing large pre-trained models for inference speed acceleration. Previous pruning approaches rely on access to the original training dat…
When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks
Haihang Wu, Wei Wang, Tamasha Malepathirana +3
Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks. One crucial aspect of ne…
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning
Tamasha Malepathirana, Damith Senanayake, Saman Halgamuge
Catastrophic forgetting; the loss of old knowledge upon acquiring new knowledge, is a pitfall faced by deep neural networks in real-world applications. Many prevailing solutions to…